Book Image

Interactive Visualization and Plotting with Julia

By : Diego Javier Zea
Book Image

Interactive Visualization and Plotting with Julia

By: Diego Javier Zea

Overview of this book

The Julia programming language offers a fresh perspective into the data visualization field. Interactive Visualization and Plotting with Julia begins by introducing the Julia language and the Plots package. The book then gives a quick overview of the Julia plotting ecosystem to help you choose the best library for your task. In particular, you will discover the many ways to create interactive visualizations with its packages. You’ll also leverage Pluto notebooks to gain interactivity and use them intensively through this book. You’ll find out how to create animations, a handy skill for communication and teaching. Then, the book shows how to solve data analysis problems using DataFrames and various plotting packages based on the grammar of graphics. Furthermore, you’ll discover how to create the most common statistical plots for data exploration. Also, you’ll learn to visualize geographically distributed data, graphs and networks, and biological data. Lastly, this book will go deeper into plot customizations with Plots, Makie, and Gadfly—focusing on the former—teaching you to create plot themes, arrange multiple plots into a single figure, and build new plot types. By the end of this Julia book, you’ll be able to create interactive and publication-quality static plots for data analysis and exploration tasks using Julia.
Table of Contents (19 chapters)
1
Section 1 – Getting Started
6
Section 2 – Advanced Plot Types
12
Section 3 – Mastering Plot Customization

Introducing StatsPlots

Before describing different statistical plots, let’s introduce the main Julia package we will use in this chapter: the StatsPlots package. It defines plotting recipes to create statistical plots using the Plots package. It also adds the @df macro to support the DataFrames package. Furthermore, the StatsPlots package offers type recipes for some of the types defined on packages from the JuliaStats organization. Among those, we can find the Clustering, Distributions, and MultivariateStats packages. For example, we can call the plot function on a Cauchy object from the Distributions package to plot our Cauchy distribution. We will learn more about type recipes in Chapter 14, Designing Your Own Plots – Plot Recipes.  Let’s explore the syntax and basic features of the StatsPlots package using the Iris dataset and Pluto:

  1. Create a new Pluto notebook and execute using RDatasets in the first cell.
  2. In a new cell, execute iris...